---
title: Manage prompts programmatically
sidebarTitle: Manage prompts programmatically
---

You can use the LangSmith Python and TypeScript SDK to manage prompts programmatically.

<Note>
Previously this functionality lived in the `langchainhub` package which is now deprecated. All functionality going forward will live in the `langsmith` package.
</Note>

## Install packages

In Python, you can directly use the LangSmith SDK (*recommended, full functionality*) or you can use through the LangChain package (limited to pushing and pulling prompts).

In TypeScript, you must use the LangChain npm package for pulling prompts (it also allows pushing). For all other functionality, use the LangSmith package.

<CodeGroup>
```bash pip
pip install -U langsmith # version >= 0.1.99
pip install -U langchain langsmith # langsmith version >= 0.1.99 and langchain >= 0.2.13
```

```bash uv
uv add langsmith  # version >= 0.1.99
uv add langchain langsmith # langsmith version >= 0.1.99 and langchain >= 0.2.13
```

```bash TypeScript
yarn add langsmith langchain // langsmith version >= 0.1.99 and langchain version >= 0.2.14
```
</CodeGroup>

## Configure environment variables

If you already have `LANGSMITH_API_KEY` set to your current workspace's api key from LangSmith, you can skip this step.

Otherwise, get an API key for your workspace by navigating to `Settings > API Keys > Create API Key` in LangSmith.

Set your environment variable.

```bash
export LANGSMITH_API_KEY="lsv2_..."
```

<Note>
What we refer to as "prompts" used to be called "repos", so any references to "repo" in the code are referring to a prompt.
</Note>

## Push a prompt

To create a new prompt or update an existing prompt, you can use the `push prompt` method.

<CodeGroup>

```python Python
from langsmith import Client
from langchain_core.prompts import ChatPromptTemplate

client = Client()
prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}")
url = client.push_prompt("joke-generator", object=prompt)
# url is a link to the prompt in the UI
print(url)
```

```python LangChain (Python)
from langchain import hub as prompts
from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}")
url = prompts.push("joke-generator", prompt)
# url is a link to the prompt in the UI
print(url)
```

```typescript TypeScript
import * as hub from "langchain/hub";
import { ChatPromptTemplate } from "@langchain/core/prompts";

const prompt = ChatPromptTemplate.fromTemplate("tell me a joke about {topic}");
const url = hub.push("joke-generator", {
  object: prompt,
});
// url is a link to the prompt in the UI
console.log(url);
```

</CodeGroup>

You can also push a prompt as a RunnableSequence of a prompt and a model. This is useful for storing the model configuration you want to use with this prompt. The provider must be supported by the LangSmith playground. (see settings here: [Supported Providers](https://langsmith.com/playground))

<CodeGroup>

```python Python
from langsmith import Client
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

client = Client()
model = ChatOpenAI(model="gpt-4o-mini")
prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}")
chain = prompt | model
client.push_prompt("joke-generator-with-model", object=chain)
```

```python LangChain (Python)
from langchain import hub as prompts
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

model = ChatOpenAI(model="gpt-4o-mini")
prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}")
chain = prompt | model
url = prompts.push("joke-generator-with-model", chain)
# url is a link to the prompt in the UI
print(url)
```

```typescript TypeScript
import * as hub from "langchain/hub";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { ChatOpenAI } from "@langchain/openai";

const model = new ChatOpenAI({ model: "gpt-4o-mini" });
const prompt = ChatPromptTemplate.fromTemplate("tell me a joke about {topic}");
const chain = prompt.pipe(model);
await hub.push("joke-generator-with-model", {
  object: chain,
});
```

</CodeGroup>

## Pull a prompt

To pull a prompt, you can use the `pull prompt` method, which returns a the prompt as a langchain `PromptTemplate`.

To pull a **private prompt** you do not need to specify the owner handle (though you can, if you have one set).

To pull a **public prompt** from the LangChain Hub, you need to specify the handle of the prompt's author.

<CodeGroup>

```python Python
from langsmith import Client
from langchain_openai import ChatOpenAI

client = Client()
prompt = client.pull_prompt("joke-generator")
model = ChatOpenAI(model="gpt-4o-mini")
chain = prompt | model
chain.invoke({"topic": "cats"})
```

```python LangChain (Python)
from langchain import hub as prompts
from langchain_openai import ChatOpenAI

prompt = prompts.pull("joke-generator")
model = ChatOpenAI(model="gpt-4o-mini")
chain = prompt | model
chain.invoke({"topic": "cats"})
```

```typescript TypeScript
import * as hub from "langchain/hub";
import { ChatOpenAI } from "@langchain/openai";

const prompt = await hub.pull("joke-generator");
const model = new ChatOpenAI({ model: "gpt-4o-mini" });
const chain = prompt.pipe(model);
await chain.invoke({"topic": "cats"});
```

</CodeGroup>

Similar to pushing a prompt, you can also pull a prompt as a RunnableSequence of a prompt and a model. Just specify include\_model when pulling the prompt. If the stored prompt includes a model, it will be returned as a RunnableSequence. Make sure you have the proper environment variables set for the model you are using.

<CodeGroup>

```python Python
from langsmith import Client

client = Client()
chain = client.pull_prompt("joke-generator-with-model", include_model=True)
chain.invoke({"topic": "cats"})
```

```python LangChain (Python)
from langchain import hub as prompts

chain = prompts.pull("joke-generator-with-model", include_model=True)
chain.invoke({"topic": "cats"})
```

```typescript TypeScript
import * as hub from "langchain/hub";
import { Runnable } from "@langchain/core/runnables";

const chain = await hub.pull<Runnable>("joke-generator-with-model", { includeModel: true });
await chain.invoke({"topic": "cats"});
```

</CodeGroup>

When pulling a prompt, you can also specify a specific commit hash or [commit tag](/langsmith/manage-prompts#commit-tags) to pull a specific version of the prompt.

<CodeGroup>

```python Python
prompt = client.pull_prompt("joke-generator:12344e88")
```

```python LangChain (Python)
prompt = prompts.pull("joke-generator:12344e88")
```

```typescript TypeScript
const prompt = await hub.pull("joke-generator:12344e88")
```

</CodeGroup>

To pull a public prompt from the LangChain Hub, you need to specify the handle of the prompt's author.

<CodeGroup>

```python Python
prompt = client.pull_prompt("efriis/my-first-prompt")
```

```python LangChain (Python)
prompt = prompts.pull("efriis/my-first-prompt")
```

```typescript TypeScript
const prompt = await hub.pull("efriis/my-first-prompt")
```

</CodeGroup>

<Note>
For pulling prompts, if you are using Node.js or an environment that supports dynamic imports, we recommend using the `langchain/hub/node` entrypoint, as it handles deserialization of models associated with your prompt configuration automatically.

If you are in a non-Node environment, "includeModel" is not supported for non-OpenAI models and you should use the base `langchain/hub` entrypoint.
</Note>

## Use a prompt without LangChain

If you want to store your prompts in LangSmith but use them directly with a model provider's API, you can use our conversion methods. These convert your prompt into the payload required for the OpenAI or Anthropic API.

These conversion methods rely on logic from within LangChain integration packages, and you will need to install the appropriate package as a dependency in addition to your official SDK of choice. Here are some examples:

### OpenAI

<CodeGroup>

```bash Python
pip install -U langchain_openai
```

```bash TypeScript
yarn add @langchain/openai @langchain/core // @langchain/openai version >= 0.3.2
```

</CodeGroup>

<CodeGroup>

```python Python
from openai import OpenAI
from langsmith.client import Client, convert_prompt_to_openai_format

# langsmith client
client = Client()
# openai client
oai_client = OpenAI()

# pull prompt and invoke to populate the variables
prompt = client.pull_prompt("joke-generator")
prompt_value = prompt.invoke({"topic": "cats"})
openai_payload = convert_prompt_to_openai_format(prompt_value)
openai_response = oai_client.chat.completions.create(**openai_payload)
```

```typescript TypeScript
import * as hub from "langchain/hub";
import { convertPromptToOpenAI } from "@langchain/openai";
import OpenAI from "openai";

const prompt = await hub.pull("jacob/joke-generator");
const formattedPrompt = await prompt.invoke({
  topic: "cats",
});
const { messages } = convertPromptToOpenAI(formattedPrompt);

const openAIClient = new OpenAI();
const openAIResponse = await openAIClient.chat.completions.create({
  model: "gpt-4o-mini",
  messages,
});
```

</CodeGroup>

### Anthropic

<CodeGroup>

```bash Python
pip install -U langchain_anthropic
```

```bash TypeScript
yarn add @langchain/anthropic @langchain/core // @langchain/anthropic version >= 0.3.3
```

</CodeGroup>

<CodeGroup>

```python Python
from anthropic import Anthropic
from langsmith.client import Client, convert_prompt_to_anthropic_format

# langsmith client
client = Client()
# anthropic client
anthropic_client = Anthropic()

# pull prompt and invoke to populate the variables
prompt = client.pull_prompt("joke-generator")
prompt_value = prompt.invoke({"topic": "cats"})
anthropic_payload = convert_prompt_to_anthropic_format(prompt_value)
anthropic_response = anthropic_client.messages.create(**anthropic_payload)
```

```typescript TypeScript
import * as hub from "langchain/hub";
import { convertPromptToAnthropic } from "@langchain/anthropic";
import Anthropic from "@anthropic-ai/sdk";

const prompt = await hub.pull("jacob/joke-generator");
const formattedPrompt = await prompt.invoke({
  topic: "cats",
});
const { messages, system } = convertPromptToAnthropic(formattedPrompt);

const anthropicClient = new Anthropic();
const anthropicResponse = await anthropicClient.messages.create({
  model: "claude-3-haiku-20240307",
  system,
  messages,
  max_tokens: 1024,
  stream: false,
});
```

</CodeGroup>

## List, delete, and like prompts

You can also list, delete, and like/unlike prompts using the `list prompts`, `delete prompt`, `like prompt` and `unlike prompt` methods. See the [LangSmith SDK client](https://github.com/langchain-ai/langsmith-sdk) for extensive documentation on these methods.

<CodeGroup>

```python Python
# List all prompts in my workspace
prompts = client.list_prompts()

# List my private prompts that include "joke"
prompts = client.list_prompts(query="joke", is_public=False)

# Delete a prompt
client.delete_prompt("joke-generator")

# Like a prompt
client.like_prompt("efriis/my-first-prompt")

# Unlike a prompt
client.unlike_prompt("efriis/my-first-prompt")
```

```typescript TypeScript
// List all prompts in my workspace
import Client from "langsmith";

const client = new Client({ apiKey: "lsv2_..." });
const prompts = client.listPrompts();

for await (const prompt of prompts) {
  console.log(prompt);
}

// List my private prompts that include "joke"
const private_joke_prompts = client.listPrompts({ query: "joke", isPublic: false});

// Delete a prompt
client.deletePrompt("joke-generator");

// Like a prompt
client.likePrompt("efriis/my-first-prompt");

// Unlike a prompt
client.unlikePrompt("efriis/my-first-prompt");
```

</CodeGroup>
